一个集成高斯混合模型-支持向量机-统计模式的适合中低浓度的跃移沙粒识别算法

李浩强, 李惠娟, 杨倩文, 梅凡民

A hybrid algorithm for aeolian saltating particle recognition under low-medium particle concentrations with Gaussian Mixture Model， Support Vector Machine and probability-distribution of saltating particles' geometric and color parameters

Haoqiang Li, Huijuan Li, Qianwen Yang, Fanmin Mei

图4 新算法和发表的风沙颗粒识别算法的比较（GT、DGT、GMM、GMM+SVM分别是指基于Matlab 平台的灰度阈值算法［10］、动态灰度阈值算法［14］、高斯混合模型、高斯混合模型-支持向量机。改进YOLOv5［12］ 和YOLOv8［11］来自文献）

Fig.4 Comparisons between the new algorithm and other published schemes for saltating particle recognition in recall and accuracy rates （GT， DGT、 GMM、 GMM+SVM correspond to the gray threshold scheme from Matlab ［10］， dynamic threshold scheme ［14］， Gaussian mixture model ［16］ and improved Gaussian mixture model. The data of YOLOv5 and YOLOv8 derive from the Ref.12 and the Ref.11， respectively）